Characterisation of spatio-temporal trend in temperature extremes for environmental decision making in Bangladesh
Bibliographic record
Abstract
Spatial and sequential variability of extreme temperature events enthral the scientific community owing to their significant impact on global climate change. This study analysed trends in monthly data of temperature extremes of 23 meteorological stations of Bangladesh using Mann-Kendall test. Most of the stations showed significant increasing trend for both temperature extremes on monthly and annual scales. Most of the change points were detected during the last four decades and showed an upward trend. The results obtained from Sen's estimator vouchsafed that magnitudes of trend ranged from 0.007°C to 0.034°C per year and 0.014°C to 0.049°C per year for minimum and maximum temperature, respectively. The upward trend in both extreme temperatures pointed to global warming. The maximum number of significant trends was observed in monsoon and post-monsoon seasons for average maximum temperature. The upward trend in the monsoon and post-monsoon season may cause the drought and late winter in Bangladesh.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".